Background

• Doctoral candidate, CIT Graduate Center,  TUM Graduate School - 
• Research Associate (Wissenschaftlicher Mitarbeiter) at TUM SCCS since June 2018
• M.Sc. with Honours in Computational Science and Engineering, Technical University of Munich, 2018
• Diploma in Electrical Engineering, University of Belgrade, Signals and Systems Department, 2014

Research interests

My research focuses on applied and computational mathematics, particularly on uncertainty quantification (UQ), modeling and system identification, inverse problems, and data-driven model learning. The main applications driving my research are from hydrology. More precisely, in my work, I am bridging the gap between theoretical work on High-dimensional Uncertainty Quantification and Bayesian Inversion, applied to relatively simple simulation models, and more complex real-world problems.

• High-dimensional Forward Uncertainty Quantification and Sensitivity Analysis (mainly, analysis of conceptual distributed hydrologic models)
• Sparse Grids Methods
• Inverse problems - Bayesian Inference
• Machine Learning

Open and running student projects

Runnin student projects

• Erik A. Maurer: "Applying recurrent neural networks (RNNs) in the field of hydrology to explore uncertainty in time series forecasts and enhance theory-based models". School of Engineering and Design of the Technical University of Munich. Since June 2024
• Danylo Movchan: "Large Scale Outdoor Scene Reconstruction with 3D Gaussian Splatting". Master's Thesis, CIT School - Computer Science Department; in collaboration with Stanford University. Since October 2024

Open student projects

If you are interested in a student project (Bachelor's or Master's Thesis or anything else), it is the best to contact me directly. Here is the list of some projects that I would offer at the moment (the list is not exhaustive):

1. "UQ and SA of Hydrologic model HBV using pyApprox software tool"

   • Relevant Sources:

     • https://sandialabs.github.io/pyapprox/auto_tutorials/index.html

2. "Calibration under uncertainty of the HBV hydrologic model using a parallel implementation of the Metropolis-Hastings algorithm"

   • Relevant Sources:

     • "A general construction for parallelizingMetropolis−Hastings algorithms" 

     • "High-Performance Uncertainty Quantification with Parallelized Multilevel Markov Chain Monte Carlo" 

     • MUQ librabry

3. "Running Uncertainty Quantification simulations using the UM-Bridge software tool: application on a hydrologic model"

   • Relevant Sources:

     • https://github.com/UM-Bridge/umbridge

4. Mapping sparse grid interpolation to pseudospectral approximation 

   • Sparse Pseudospectral Approximation Method

   • Adaptive Smolyak Pseudospectral Approximations

5. Calibration under uncertainty of the simple hydrologic model using different software tools, e.g., DREAM (Dakota), MUQ

You can also come to my office and discuss possible topics. 

See also the list of Student Projects at our chair, e.g.,  https://www.in.tum.de/i05/jobangebote-studentische-projektarbeiten/job-offers-student-projects/uncertainty-quantification/

Do you want to know what other students are working on in our chair? You are warmly encouraged to attend their presentations at the SCCS Colloquium! Come to get ideas, meet your potential supervisor, or to learn from the style of others for your own presentation.